I am completely astounded by #ChatGPT's capabilities (chat.openai.com). I've been playing around with it to explore possible use cases for psychological research. Here are just a few: 1) Stimuli generation. ImageImageImage
2) Scale development: Generating potential questionnaire items. ImageImage
3) Providing a "template" for various academic writing tasks. This could be a great teaching tool. ImageImage
(Of course, ChatGPT can also write the entire research proposal...but 🤫) In this case, interestingly, it got the IV and DV mixed up. Could still be a useful teaching tool to help students learn the research proposal format, instead of working from a blank page. ImageImage
4) More stimulus generation (this time, focusing on persuasive messages): 🤯🤯🤯 ImageImage
5) Facilitating personality change/goal pursuit: Sometimes when you want to change yourself, it might be difficult to imagine how exactly you would go about doing so. AI can help you come up with strategies! (These could also be used to develop intervention instructions). ImageImage
6) Understanding the key features of a construct (e.g., honesty/dishonesty), based on how the concept is used in natural language. @CharacterGap you may find this interesting! ImageImageImage
You could probably also use the summarizing capabilities to extract themes from participants' open-ended responses! @kkrttr maybe we should try this for comparison.
I might add some more use cases as I think of them, but for now, here are some meta-thoughts: 1) It's made me question how we typically develop stimuli/questionnaire items. Instead of relying on the idiosyncrasies/biases of a small team of researchers/RAs...
ChatGPT uses SO much more information to come up with plausible stimuli. I can imagine this really helping with the issue of stimulus sampling/representativeness, and thus, generalizability.
2) There's an urgent need to develop publication/ethics guidelines for disclosing the use of AI tools in research. Indeed, if you use ChatGPT to generate stimuli, you would have to fully describe the process as part of the Method section.
3) Many RA-intensive tasks (e.g., coding open-ended responses, extracting key themes from open-ended responses) could be largely automated, pending validity evidence that compares ChatGPT with human coding. As ChatGPT currently stores the data for its own use...
We need to think more about the ethical implications of sharing participant data with ChatGPT, and update IRB applications and consent forms accordingly. Perhaps in the future, ChatGPT can be licensed for research/applied use without the data being stored/shared with @OpenAI.
4) I am fairly worried about the prospect of online participants using ChatGPT to generate essay/open-ended text responses. Will we find ways to prevent or detect this, or will we only be able to use writing tasks in the lab going forward?
5) But, despite these and other complexities, I cannot remember the last time I experienced such a sense of amazement and wonder. I'm so glad to be alive in a time of such rapid technological progress, and am excited to find ways to use this as a tool for psychological research!

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More from @JessieSunPsych

Feb 18
#SPSP2022 In collaboration with @TEDTalks "mystery experiment", @DunnHappyLab studied the effects of giving $10,000 to 200 people to spend however they wanted (plus 100 in no-$ control condition). Really striking findings!! 1/n
Money does buy happiness (even 3 months after the money has been spent)! #SPSP2022 2/n
Red bars = how much life satisfaction $2 mil would have provided a millionaire couple; Blue bars = life satisfaction gains from redistributing that to 200 people ($10,000 each). Love this visualization/effect size quantification!! #SPSP2022 3/n
Read 5 tweets
Oct 10, 2021
Thought I'd share some miscellaneous thoughts/lessons from two rounds on the job market: 🧵 1/n
Be aware that out-of-pocket costs can add up! Some universities made me book my own flights (including an international flight!), & I had to wait weeks for reimbursement. In one case, I also ended up staying a few extra nights in b/t interviews (instead of flying home first). 2/n
The lesson is that universities should book flights if at all possible to make it easier on job candidates, and advisors can also support their students (e.g., by offering to pay for extra nights of accommodation out of their research budget). 3/n
Read 18 tweets
Nov 29, 2020
Great thread. The dominance of Likert-type outcome measures means that most psychologists don't actually know what a non-arbitrary measure looks like. So here's a thread on how we can make our measures less arbitrary: 1/n
Blanton & Jaccard's (2006) paper revolutionised my thinking about effect sizes and meaningful metrics in psychology. I highly recommend it. psycnet.apa.org/record/2006-00… 2/n
Their key argument is that a metric is arbitrary when it provides no information about where a person is located on the true underlying continuum, or what a 1-unit change means. 3/n
Read 11 tweets
May 15, 2020
Fascinating preprint, @DHBostyn & @xphilosopher! A few journal club thoughts from me and @AllardThuriot (Thread): 1/n
1) We loved the goal of mathematically modelling different shapes of blame/praise judgments, and identifying neutral points. I saw this as very much in line w/ calls to reduce the arbitrariness of measures and define the continua of constructs, e.g.: psycnet.apa.org/record/2006-00… 2/n
Read 14 tweets

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